{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Upload dataset (.dta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "use \"https://github.com/SaoriIwa/Stata-IE-Visual-Library/raw/master/Library/Bar%20plots/Bar%20plot%20of%20multiple%20variables/data.dta\", clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.  We will create mean of all crop observations across each ``treatment_group``, baseline, and midline category. \n",
    "First we collapse baseline and midline variables to get Treatment/Control mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "collapse bl_* ml_*, by(treatment_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Reshape dataset to a long-format, indexed by the new \"crop\" variable. This is a string variable with the name of the crop.\n",
    "\n",
    "(e.g. variables ``ml_w_Barley_Prod`` and ``bl_w_Barley_Prod``, ``w_Barley_Prod`` becomes the value of the new `crop` variable.) \n",
    "\n",
    "\n",
    "End product: data with columns of \n",
    "\n",
    "    treatment_group\n",
    "    crop\n",
    "    bl_ \n",
    "    ml_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui reshape long bl_ ml_, i(treatment_group) j(crop) str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Reshape again dataset to wide-format, indexed by \"treatment_group\" for each baseline and midline values.\n",
    "\n",
    "For each value of ``crop``, the code takes variable ``treatment_group``, and creates merged variable with ``bl_`` and ``ml_`` variables.\n",
    "(e.g. Each ``bl_`` variable will now have two types, ``bl_0 `` and ``bl_1``, using values from ``treatment_group``, which is 0 and 1.)\n",
    "\n",
    "Data will have columns of:\n",
    "\n",
    "    crop\n",
    "    bl_0\n",
    "    bl_1\n",
    "    ml_0\n",
    "    ml_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui reshape wide bl_ ml_, i(crop) j(treatment_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Keeping only the variables of interests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsort -ml_1\n",
    "qui keep if _n<4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Creating position vector for bars for the y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui gen xml_1 = _n\n",
    "qui gen xml_0 = xml_1 +.2\n",
    "qui gen xbl_1 = xml_1 +.4\n",
    "qui gen xbl_0 = xml_1 +.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Cleaning variables for the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      ". qui replace crop = subinstr(crop, \"_\", \" \",.) \n",
      "\n",
      ". qui replace crop = subinstr(crop, \"w\", \" \",.)\n"
     ]
    }
   ],
   "source": [
    "qui foreach var of varlist bl_* ml_* {\n",
    "\treplace `var' = round(`var',.1)\n",
    "\t}\n",
    "\n",
    "qui replace crop = subinstr(crop, \"_\", \" \",.) \n",
    "qui replace crop = subinstr(crop, \"w\", \" \",.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Inserting the list of values in the local macro macname "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "qui forvalues j = 1/3 {\n",
    "\tlevelsof crop if _n==`j', local(crop`j')\n",
    "\t}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Creating a horizontal bar graph with cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "twoway bar bl_0 xbl_0, horizontal bcolor(edkblue) barwidth(.2) || ///\n",
    "\tbar ml_0 xml_0, horizontal bcolor(eltblue) barwidth(.2) || ///\n",
    "\tscatter xml_0 ml_0, mlabel(ml_0) mlabcolor(black) msymbol(none) mlabsize(vsmall) || ///\n",
    "\tscatter xbl_0 bl_0, mlabel(bl_0) mlabcolor(black) msymbol(none) mlabsize(vsmall) || ///\n",
    "\tbar bl_1 xbl_1, horizontal bcolor(emidblue) barwidth(.2) || ///\n",
    "\tbar ml_1 xml_1, horizontal bcolor(black) barwidth(.2) || ///\n",
    "\tscatter xml_1 ml_1, mlabel(ml_1) mlabcolor(black) msymbol(none) mlabsize(vsmall) || ///\n",
    "\tscatter xbl_1 bl_1, mlabel(bl_1) mlabcolor(black) msymbol(none) mlabsize(vsmall) ///\n",
    "\txlabel(0 \"0\" 50 \"50\" 100 \"100\" 150 \"150\") ///\n",
    "\txscale(range(0(20)100)) /// \n",
    "\tylabel(1.3 `crop1' 2.3 `crop2' 3.3 `crop3', angle(0)) ///\n",
    "\tgraphregion(color(white)) bgcolor(white) ///\n",
    "\tlegend(label(1 \"Baseline-Control\") label(2 \"Midline-Control\") label(5 \"Baseline-Treatment\") label(6 \"Midline-Treatment\") order(1 5 2 6)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Exporting the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> er xbl_1 bl_1, mlabel(bl_1) mlabcolor(black) msymbol(none) mlabsize(vsmall)  xlabel(0 \"0\" 50 \"50\" 100 \"100\" 150 \"150\")  xscale(range(0(20)100))  ylabel(1.3 `crop1' 2.3 `crop2' 3.3 `crop3', angle(0))  graphregion(color(white)) bgcolor(white)  legend(labe\n",
      "> l(1 \"Baseline-Control\") label(2 \"Midline-Control\") label(5 \"Baseline-Treatment\") label(6 \"Midline-Treatment\") order(1 5 2 6))\n",
      "\n",
      ". noi gr export C:/Users/Saori/.stata_kernel_cache/graph${stata_kernel_graph_counter}.png, width(600) replace        \n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "This front-end cannot display the desired image type."
      ]
     },
     "metadata": {
      "image/png": {
       "height": 436,
       "width": 600
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      ". global stata_kernel_graph_counter = $stata_kernel_graph_counter + 1        \n",
      "\n",
      ". `2324dafda88127c6b88efc156b1487df'\n",
      "(file figure.png written in PNG format)\n"
     ]
    }
   ],
   "source": [
    "graph export \"figure.png\", replace"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Stata",
   "language": "stata",
   "name": "stata"
  },
  "language_info": {
   "file_extension": ".do",
   "mimetype": "text/x-stata",
   "name": "stata"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
